Triple
T2271606
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | London Waterloo railway station |
E50670
|
entity |
| Predicate | hasSuburbanPlatforms |
P37599
|
FINISHED |
| Object | 5 |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: 5 | Statement: [London Waterloo railway station, hasSuburbanPlatforms, 5]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: hasSuburbanPlatforms Context triple: [London Waterloo railway station, hasSuburbanPlatforms, 5]
-
A.
hasSuburbanSection
Indicates that a larger route, line, or area includes a portion that passes through or serves a suburban region.
-
B.
hasSuburbanAreas
Indicates that a place includes or is associated with surrounding residential suburban districts or neighborhoods.
-
C.
isSuburbanStationOf
Indicates that a station is located in a suburban area and functionally serves as a subsidiary or outlying station of a main or central station.
-
D.
hasSuburbanCharacter
Indicates that something possesses qualities or features typically associated with suburban areas, such as lower density, residential focus, and car-oriented development.
-
E.
hasIslandPlatforms
Indicates that the subject has one or more island-style platforms, typically positioned between tracks and accessible from both sides.
- F. None of above. chosen
Provenance (4 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a88b05910c8190a9a2b1ff230c85f9 |
completed | March 4, 2026, 7:41 p.m. |
| NER | Named-entity recognition | batch_69abc39c6ff0819081a07696f1c29990 |
completed | March 7, 2026, 6:20 a.m. |
| PD | Predicate disambiguation | batch_69abbdb7719081909143efa8f48df4e4 |
completed | March 7, 2026, 5:55 a.m. |
| PDg | Predicate description generation | batch_69abc39b2f548190a38f604e0d36db3a |
completed | March 7, 2026, 6:20 a.m. |
Created at: March 4, 2026, 7:48 p.m.